CKEMI: Concept knowledge enhanced metaphor identification framework

Dian Wang, Yang Li, Suge Wang, Xin Chen, Jian Liao, Deyu Li, Xiaoli Li

Abstract: Metaphor is pervasive in our life, there is roughly one metaphor every three sentences on average in our daily conversations. Previous metaphor identification researches in NLP have rarely focused on similarity between concepts from different domains. In this paper, we propose a Concept Knowledge Enhanced Metaphor Identification Framework (CKEMI) to model similarity between concepts from different domains. First, we construct the descriptive concept word set and the inter-word relation concept word set by selecting knowledge from the ConceptNet knowledge base. Then, we devise two hierarchical relation concept graph networks to refine inter-word relation concept knowledge. Next, we design the concept consistency mapping function to constrain the representation of inter-word relation concept and learn similarity information between concepts. Finally, we construct the target domain semantic scene by integrating the representation of inter-word relation concept knowledge for metaphor identification. Specifically, the F1 score of CKEMI is superior to the state-of-the-art (SOTA) methods, achieving improvements of over 0.5%, 1.0%, and 1.2% on the VUA-18(10k), VUA-20(16k), and MOH-X(0.6k) datasets, respectively.